Text-to-Image
Diffusers
TensorBoard
lora
diffusers-training
stable-diffusion
stable-diffusion-diffusers
Instructions to use PhucNT2511/db_lora_irit_diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use PhucNT2511/db_lora_irit_diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("SG161222/Realistic_Vision_V6.0_B1_noVAE", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("PhucNT2511/db_lora_irit_diffusers") prompt = "a photo of shs woman" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
LoRA DreamBooth - PhucNT2511/db_lora_irit_diffusers
These are LoRA adaption weights for SG161222/Realistic_Vision_V6.0_B1_noVAE. The weights were trained on a photo of shs woman using DreamBooth. You can find some example images in the following.
LoRA for the text encoder was enabled: False.
Intended uses & limitations
How to use
# TODO: add an example code snippet for running this diffusion pipeline
Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
Training details
[TODO: describe the data used to train the model]
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Model tree for PhucNT2511/db_lora_irit_diffusers
Base model
SG161222/Realistic_Vision_V6.0_B1_noVAE


